code: def _index_param_value(X, v, indices):
      """Private helper function for parameter value indexing."""
      if not _is_arraylike(v) or _num_samples(v) != _num_samples(X):
          # pass through: skip indexing
          return v
      if sp.issparse(v):
          v = v.tocsr()
      return safe_indexing(v, indices)
  
imports: from __future__ import print_function
  from __future__ import division
  import warnings
  import numbers
  import time
  import numpy
  import scipy.sparse
  from ..base import is_classifier, clone
  from ..utils import indexable, check_random_state, safe_indexing
  from ..utils.fixes import astype
  from ..utils.validation import _is_arraylike, _num_samples
  from ..utils.metaestimators import _safe_split
  from ..externals.joblib import Parallel, delayed, logger
  from ..metrics.scorer import check_scoring
  from ..exceptions import FitFailedWarning
  from ._split import check_cv
label_desc: Use image node_id 7 for calling a global function with example usage: _index_param_value(X, v, indices) and returns: safe_indexing, v